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Temperature Abnormality Monitoring Model for Industrial Equipment-Based Image and Infrared Thermography
Ting-Yi Chiang, I-long Lin
128 - 133
10.3233/FAIA250114
Research Article
Frontiers in Artificial Intelligence and Applications
Volume 404: Artificial Intelligence and Human-Computer Interaction
Abstract

The study proposed a model for monitoring temperature anomalies in industrial machinery based on image and thermography data, focusing on motors and electrical panels. Enhance the operational stability of the machine through multimodal data fusion analysis using Convolutional Neural Networks (CNN). The model includes four parts: data collection, data preprocessing, and anomaly detection model training and deployment.

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